• Artificial Intelligence
  • Pallavi Mate
  • FEB 07, 2018

Machine Learning Trends

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Machine Learning (ML) has revolutionized the world of computers by allowing them to learn as they progress forward with large data sets, thus mitigating many previous programming pitfalls and impasses. Machine Learning builds algorithms, which when exposed to high volumes of data, can self-teach and evolve. When this unique technology powers Artificial Intelligence (AI) applications, the combination can be powerful. We can soon expect to see smart robots around us doing all our jobs – much quicker, much more accurately, and even improving themselves at every step. Will this world need intelligent humans anymore or shall we soon be outclassed by self-thinking robots?

Machine Learning Trends in Research

In the research areas, Machine Learning is steadily moving away from abstractions and engaging more in business problem solving with support from AI and Deep Learning. In What Is the Future of Machine Learning, Forbes predicts the theoretical research in ML will gradually pave the way for business problem solving. With Big Data making its way back to mainstream business activities, now smart (ML) algorithms can simply use massive loads of both static and dynamic data to continuously learn and improve for enhanced performance.

ML Application Development Trends

The combined AI and advanced ML practice that ignited about four years ago and since continued unscathed, will dominate Artificial Intelligence application development. This lethal combination will deliver more systems that “understand, learn, predict, adapt and potentially operate autonomously. “ Cheap hardware, cheap memory, cheap storage technologies, more processing power, superior algorithms, and massive data streams will all contribute to the success of ML-powered AI applications. There will be steady rise in Ml-powered AI application in industry sectors like preventive healthcare, banking, finance, and media. For businesses that means more automated functions and fewer human checkpoints. 

Democratization of Machine Learning in the Cloud          

Democratization of AI and ML through Cloud technologies, open standards, and algorithm economy will continue. The growing trend of deploying prebuilt ML algorithms to enable Self-Service Business Intelligence and Analytics is a positive step towards democratization of ML. In Google Says Machine Learning is the Future, the author champions the democratization of ML through idea sharing. A case in point is Google’s Tensor Flow, which has championed the need for open standards in Machine Learning. This article claims that almost anyone with a laptop and an Internet connection can dare to be a Machine Learning expert today provided they have the right mind set.

The provisioning of Cloud-based IT services was already a good step to make advanced Data Science a mainstream activity, and now with Cloud and packaged algorithms, mid-sized ad smaller businesses will have access to Self-Service BI and Analytics, which was till now only a dream. Also, the mainstream business users will gradually take an active role in data-centric business systems. Machine Learning Trends – Future AI  claims that more enterprises will capitalize on the Machine Learning Cloud and do their part to lobby for democratized data technologies.

Demand-Supply Gaps in Data Science and Machine Learning will Rise

The business world is steadily heading toward the prophetic 2018, when according to McKinsey the first void in data technology expertise will be felt in US and then gradually in the rest of the world. The demand-supply gap in Data Science and Machine Learning skills will continue to rise till academic programs and industry workshops begin to produce a ready workforce. In response to this sharp rise in demand-supply gap, more enterprises and academic institutions will collaborate to train future Data Scientists and ML experts. This kind of training will compete with the traditional Data Science classroom, and will focus more on practical skills rather than on theoretical knowledge. KDNuggets will continue to challenge the curious mind by publishing articles like 10 Algorithms that Machine Learning Engineers Should Know . 2017 will witness a steady rise in contributions from KDNugget and Kaggle in providing alternative training to future Data Scientists and Machine Learning experts through practical skill development.   

The Algorithm Economy will take Center Stage

Over the next year or two, businesses will be using canned algorithms for all data-centric activities like BI, Predictive Analytics, and CRM. The algorithm economy, which Forbes mentions, will usher in a marketplace where all data companies will compete for a space. In 2017, global businesses will engage in Self-Service BI, and experience the growth of algorithmic business solutions, and ML in the Cloud. So far as algorithm-driven business decision making is concerned, 2017 may actually see two distinct types of algorithm economies. On one hand, average businesses will utilize canned algorithmic models for their operational and customer-facing functions. On the other hand, proprietary ML algorithms will become a market differentiator among large, competing enterprises.

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